#scalablealgorithms arama sonuçları

Over 1,000 screens already In everything you do, make sure your design is scalable VERY SCALABLE. Especially when working on a complex or robust web app or software system, always keep scalability at the back of your mind. It’s extremely important.

Thrilled to release new paper: “Scaling Latent Reasoning via Looped Language Models.” TLDR: We scale up loop language models to 2.6 billion parameters, and pretrained on > 7 trillion tokens. The resulting model is on par with SOTA language models of 2 to 3x size.

Since compute grows faster than the web, we think the future of pre-training lies in the algorithms that will best leverage ♾ compute We find simple recipes that improve the asymptote of compute scaling laws to be 5x data efficient, offering better perf w/ sufficient compute

The first fantastic paper on scaling RL with LLMs just dropped. I strongly recommend taking a look and will be sharing more thoughts on the blog soon. The Art of Scaling Reinforcement Learning Compute for LLMs Khatri & Madaan et al.

🧵 As AI labs race to scale RL, one question matters: when should you stop pre-training and start RL? We trained 5 Qwen models (0.6B→14B) with RL on GSM8K and found something wild: Small models see EMERGENCE-LIKE jumps. Large models see diminishing returns. The scaling law?…

Wish to build scaling laws for RL but not sure how to scale? Or what scales? Or would RL even scale predictably? We introduce: The Art of Scaling Reinforcement Learning Compute for LLMs

Banger paper from Meta and collaborators. This paper is one of the best deep dives yet on how reinforcement learning (RL) actually scales for LLMs. The team ran over 400,000 GPU hours of experiments to find a predictable scaling pattern and a stable recipe (ScaleRL) that…

Graph-based Agent Planning It lets AI agents run multiple tools in parallel to accelerate task completion. Uses graphs to map tool dependencies + RL to learn the best execution order. RL also helps with scheduling strategies and planning. Major speedup for complex tasks.

SKALE Expand is coming to life with the @base Sepolia testnet chain, and will be supporting x402 in the Base ecosystem! Build on Base with zero gas fees, instant finality, and BITE protocol. 👇 📄 Docs: docs.skale.space/welcome/skale-… 📲 SKALE Builders Group: t.me/+o_7DCw9qcbI2N…

this paper costed 4.2 mil USD to write holy... most labs haven't reached the point of releasing models that costed that much let alone a paper that covers all the details

We’re excited to partner with @LMSYSOrg’s SGLang to bring LLM serving to everyone. 🔥 Parallax is a high-concurrency inference framework that lets anyone host LLMs of all sizes across heterogeneous GPUs and devices — local or remote, anywhere in the world. SGLang @LMSYSOrg…

How to Nail Great Entries Using the Scale Feature Here’s a quick tip that can seriously level up your entries - using the Scale feature. In my first two screenshots (Drift / Hyperliquid), I show how this works in practice. At its core, Scale places a series of evenly spaced…

Scale isn’t just size—it’s safe change. We break down architecture layers and how automation turns standards into shipped code, tests, and lineage. varigence.com/blog/scalable-…

Stop thinking more people = more growth. You don’t scale with headcount. You scale with systems. Lean teams. Clear vision. Ruthless execution. Alignment. Autonomy. Accountability. This isn’t hiring — it’s a scaling philosophy. ⚙️ Build Systems. Build Brands. Build Legacy. 👇

Closed models don’t scale trust. They scale dependency. Every call to an API adds value to a black box. Every dataset compounds someone else’s moat. That’s not progress. That’s extraction. The next cycle of AI won’t be won by scale. It’ll be won by alignment. and whoever builds…

Must-read SEO: This article analyzed the Google API leaks to demonstrate why Topical Authority is "the result of a precise mathematical model":

Most Know Algorithms - Two Pointers - Sliding Window - Binary Search - Prefix Sum / Difference Array - Greedy - Backtracking - Bitmasking - Union-Find (DSU) - Monotonic Stack /Queue - Topological Sort - Dynamic Programming - Divide and Conquer - Meet in the Middle - Trie

Day 3 of learning system design I found a system design repo on GitHub called system design primer, they have really detailed tutorials and documentations. Today I took a scalability lecture from a Harvard lecturer on YouTube. He talked about how scalability determines how a…

The way to analyze competing algorithms is to count the number of steps each one takes. Binary search in an ordered array drastically outperforms linear search as data size grows, cutting the number of steps from hundreds to just a handful.